標題: Comparison of TSCS regression and neural network models for panel data forecasting: debt policy
作者: Pao, HT
Chih, YY
管理科學系
Department of Management Science
關鍵字: neural networks;TSCS regression;forecasting;capital structure;panel data
公開日期: 1-Apr-2006
摘要: Empirical studies of variations in debt ratios across firms have analyzed important determinants of capital structure using statistical models. Researchers, however, rarely employ nonlinear models to examine the determinants and make little effort to identify a superior prediction model among competing ones. This paper reviews the time-series cross-sectional (TSCS) regression and the predictive abilities of neural network (NN) utilizing panel data concerning debt ratio of high-tech industries in Taiwan. We built models with these two methods using the same set of measurements as determinants of debt ratio and compared the forecasting performance of five models, namely, three TSCS regression models and two NN models. Models built with neural network obtained the lowest mean square error and mean absolute error. These results reveal that the relationships between debt ratio and determinants are nonlinear and that NNs are more competent in modeling and forecasting the test panel data. We conclude that NN models can be used to solve panel data analysis and forecasting problems.
URI: http://dx.doi.org/10.1007/s00521-005-0014-x
http://hdl.handle.net/11536/12441
ISSN: 0941-0643
DOI: 10.1007/s00521-005-0014-x
期刊: NEURAL COMPUTING & APPLICATIONS
Volume: 15
Issue: 2
起始頁: 117
結束頁: 123
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